Translating Knowledge From Systems Biology to the Bedside

Abstract and Introduction

Summary

Background Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Characterised by abnormal fat accumulation in the liver, NAFLD presents high degree of comorbidity with disorders of the metabolic syndrome, including type 2 diabetes, obesity and cardiovascular disease. These comorbidities have strong negative impact on the natural course of NAFLD and vice versa, whereby the presence of NAFLD substantially modifies the course and prognosis of metabolic syndrome-associated diseases.

Aim To use systems biology strategies to interrogate disease mechanisms that are common to NAFLD and metabolic syndrome.

Methods We mapped shared gene/protein-disease interaction networks, we performed gene-disease enrichment analysis to assess pleiotropy, and we created a gene-drug connectivity network.

Results We found that a shared network of genes/proteins is overrepresented by immune response-related pathways, post-translational modifications of nuclear receptors, and platelet-related processes, including activation and platelet signalling. Likewise, gene-based disease-enrichment analysis suggested underlying molecular effectors that are shared with major systemic disorders, including diverse autoimmune diseases, kidney, respiratory and nervous system disorders, cancer and infectious diseases. The shared list of genes/proteins was enriched in drug targets for anti-inflammatory therapy, drugs used to treat cardiovascular diseases, antimicrobial agents and phytochemicals, among many other approved pharmaceutical compounds. By leveraging on publicly available OMICs data, we were able to show that shared loci are not necessarily affected by reverse causality.

Conclusion We provide evidence indicating that NAFLD treatment, including severe histological traits, cannot be limited to the use of a single drug, as it rather requires a multi-target therapeutic approach.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide.[1,2,3] Non-alcoholic steatohepatitis (NASH) and NASH-fibrosis, the severe and progressive clinical forms of NAFLD, impose clinical challenges because they are difficult to detect in the earlier stages and their presence may predispose the affected individual to the development of cirrhosis and hepatocellular carcinoma.[1,2,3]

In addition, NAFLD presents high degree of concomitance with the Metabolic syndrome-associated disorders, including type 2 diabetes, obesity, cardiovascular disease and dyslipidemia. These comorbidities exert a strong negative impact on the natural course of NAFLD.[1,2,3] In fact, NAFLD severity is highly influenced by the presence of type 2 diabetes and obesity, both of which increase the likelihood of worsening liver fibrosis and development of end-stage liver disease.[1,2,3,4] Likewise, presence of NAFLD significantly influences not only the development of insulin resistance[5,6,7] but also the course of cardiovascular disease.[8,9,10,11,12]

Considered jointly, the aforementioned evidence suggests the existence of shared pathogenic mechanisms between NAFLD and Metabolic syndrome-associated diseases.[13] Hence, this imparts the challenge of designing and implementing treatment strategies for simultaneously targeting multiple phenotypes. It thus becomes essential not only to uncover the shared pathogenic networks that link NAFLD, NASH, and Metabolic syndrome but also to understand how they interact with each other to define combined preventive strategies and to explore novel therapeutic approaches.

Here, we hypothesise that NAFLD and the Metabolic syndrome phenotypes present common etiology and share similar underlying biological processes. In addition, factors involved in the progression of NAFLD, such as inflammation and fibrogenesis, represent common pathophenotypes implicated in the progression of complex systemic diseases. To test these hypotheses, we adopted systems biology strategies, which rely on the assumption that systemic disorders originate from the disruption of common regulatory gene networks that govern cellular and tissue physiology. This assumption is of particular relevance for explaining the presence of comorbidities associated with NAFLD. Therefore, our aim was to map gene/protein-disease interaction networks, which would conceptually allow the identification of molecular mediators that link related disorders/diseases.

Our approach first involved search of the pertinent biomedical literature related to genes and/or proteins associated with diseases of interest (NAFLD and NAFLD-severity associated phenotypes, including inflammation and fibrosis, type 2 diabetes, obesity, arterial hypertension and dyslipidemia) which was subjected to literature-enrichment analysis. This step provided us with a theoretical framework for testing the hypothesis of common disease pathogenic mechanisms between NAFLD and Metabolic syndrome. We next selected genes that are shared among all the phenotypes mentioned above, which were used to predict the presence of comorbidity with systemic diseases, as well as to explore potential genes-drugs relationships to create a gene-drug interaction network that allows predicting potential therapeutic targets.

Tables

Table 1. A common set of 50 genes that were shared among the cluster of diseases (NAFLD, type 2 diabetes, dyslipidemia, arterial hypertension, and obesity) and traits (inflammation and fibrosis)

Gene symbol

Gene name

Location

ACE

Angiotensin I converting enzyme

17q23.3

ADIPOQ

Adiponectin, C1Q and collagen domain containing

3q27.3

ADM

Adrenomedullin

11p15.4

ADRB2

adrenoceptor beta 2

5q32

AGER

Advanced glycosylation end-product specific receptor

6p21.32

AGT

Angiotensinogen

1q42.2

AGTR1

Angiotensin II receptor type 1

3q24

ANGPT2

Angiopoietin 2

8p23.1

APLN

Apelin

Xq26.1

APOA1

Apolipoprotein A1

11q23.3

CCL2

C-C motif chemokine ligand 2

17q12

CHI3L1

Chitinase 3 like 1

1q32.1

CRP

C-reactive protein

1q23.2

CST3

Cystatin C

20p11.21

DPP4

Dipeptidyl peptidase 4

2q24.2

EDN1

Endothelin 1

6p24.1

F2

Coagulation factor II, thrombin

11p11.2

FGF23

Fibroblast growth factor 23

12p13.32

GDF15

Growth differentiation factor 15

19p13.11

HFE

Homeostatic iron regulator

6p22.2

HP

Haptoglobin

16q22.2

ICAM1

Intercellular adhesion molecule 1

19p13.2

IGF1

Insulin like growth factor 1

12q23.2

IL1A

Interleukin 1 alpha

2q14.1

IL1B

Interleukin 1 beta

2q14.1

IL6

Interleukin 6

7p15.3

LCN2

Lipocalin 2

9q34.11

LEP

Leptin

7q32.1

MIF

Macrophage migration inhibitory factor

22q11.23

MMP2

Matrix metallopeptidase 2

16q12.2

MMP9

Matrix metallopeptidase 9

20q13.12

NOS3

Nitric oxide synthase 3

7q36.1

NPPB

Natriuretic peptide B

1p36.22

NR3C1

Nuclear receptor subfamily 3 group C member 1

5q31.3

PPARA

Peroxisome proliferator activated receptor alpha

22q13.31

PPARG

Peroxisome proliferator activated receptor gamma

3p25.2

PTX3

Pentraxin 3

3q25.32

RARRES2

Retinoic acid receptor responder 2

7q36.1

RBP4

Retinol binding protein 4

10q23.33

SELP

Selectin P

1q24.2

SERPINE1

Serpin family E member 1

7q22.1

SOD2

Superoxide dismutase 2

6q25.3

SPP1

Secreted phosphoprotein 1

4q22.1

TIMP1

TIMP metallopeptidase inhibitor 1

Xp11.3

TLR4

Toll like receptor 4

9q33.1

TNF

Tumour necrosis factor

6p21.33

TNFRSF11B

TNF receptor superfamily member 11b

8q24.12

TNFRSF1B

TNF receptor superfamily member 1B

1p36.22

VDR

Vitamin D receptor

12q13.11

VEGFA

Vascular endothelial growth factor A

6p21.1

The refined intersection analysis was performed using 412 genes and the final list of shared genes/proteins contained 50 terms as explained in Figure 2. Genes in the list are shown in alphabetic order.

References

Authors and Disclosures

Authors and Disclosures

Silvia Sookoian1,2 and Carlos J. Pirola1,3

1School of Medicine, Institute of Medical Research A. Lanari, University of Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina2Department of Clinical and Molecular Hepatology, National Scientific and Technical Research Council (CONICET), Institute of Medical Research (IDIM), University of Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina3Department of Molecular Genetics and Biology of Complex Diseases, National Scientific and Technical Research Council (CONICET), Institute of Medical Research (IDIM), University of Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina